arXiv (All)
open access
Prediction--Loss Alignment for Sampler--Robust Flow Matching Training
Recent work has popularized a practical recipe in diffusion and flow matching: predict the clean signal $x$, convert it to a velocity, and train through a velocity-space loss. The conversion contains a singular endpoint amplification and therefore appears prone to unstable optimization, yet recent s
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Beyond the Manifold Hypothesis: Hybrid Spectral Parameterizations for Flow Matching#1116763
- VTV-FM: Flow Matching through Variational Terminal-Velocity Closure#1155720
- Faster Inference of Flow-Based Generative Models via Improved Data-Noise Coupling#1129199
- Flow Sampling: Learning to Sample from Unnormalized Densities via Denoising Conditional Processes#155274
- When Can Conditional Flow Matching Replace Pointwise Negative Log-Likelihood?#973362
Record · ID 998105
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive